Analytics Model

from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import adjusted_rand_score
eda = pd.read_parquet("data/eda.parquet")

---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
Cell In[1], line 8
      6 from sklearn.preprocessing import LabelEncoder
      7 from sklearn.metrics import adjusted_rand_score
----> 8 eda = pd.read_parquet("data/eda.parquet")

File /opt/hostedtoolcache/Python/3.11.12/x64/lib/python3.11/site-packages/pandas/io/parquet.py:667, in read_parquet(path, engine, columns, storage_options, use_nullable_dtypes, dtype_backend, filesystem, filters, **kwargs)
    664     use_nullable_dtypes = False
    665 check_dtype_backend(dtype_backend)
--> 667 return impl.read(
    668     path,
    669     columns=columns,
    670     filters=filters,
    671     storage_options=storage_options,
    672     use_nullable_dtypes=use_nullable_dtypes,
    673     dtype_backend=dtype_backend,
    674     filesystem=filesystem,
    675     **kwargs,
    676 )

File /opt/hostedtoolcache/Python/3.11.12/x64/lib/python3.11/site-packages/pandas/io/parquet.py:267, in PyArrowImpl.read(self, path, columns, filters, use_nullable_dtypes, dtype_backend, storage_options, filesystem, **kwargs)
    264 if manager == "array":
    265     to_pandas_kwargs["split_blocks"] = True  # type: ignore[assignment]
--> 267 path_or_handle, handles, filesystem = _get_path_or_handle(
    268     path,
    269     filesystem,
    270     storage_options=storage_options,
    271     mode="rb",
    272 )
    273 try:
    274     pa_table = self.api.parquet.read_table(
    275         path_or_handle,
    276         columns=columns,
   (...)
    279         **kwargs,
    280     )

File /opt/hostedtoolcache/Python/3.11.12/x64/lib/python3.11/site-packages/pandas/io/parquet.py:140, in _get_path_or_handle(path, fs, storage_options, mode, is_dir)
    130 handles = None
    131 if (
    132     not fs
    133     and not is_dir
   (...)
    138     # fsspec resources can also point to directories
    139     # this branch is used for example when reading from non-fsspec URLs
--> 140     handles = get_handle(
    141         path_or_handle, mode, is_text=False, storage_options=storage_options
    142     )
    143     fs = None
    144     path_or_handle = handles.handle

File /opt/hostedtoolcache/Python/3.11.12/x64/lib/python3.11/site-packages/pandas/io/common.py:882, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
    873         handle = open(
    874             handle,
    875             ioargs.mode,
   (...)
    878             newline="",
    879         )
    880     else:
    881         # Binary mode
--> 882         handle = open(handle, ioargs.mode)
    883     handles.append(handle)
    885 # Convert BytesIO or file objects passed with an encoding

FileNotFoundError: [Errno 2] No such file or directory: 'data/eda.parquet'

features = eda[['SALARY', 'MAX_YEARS_EXPERIENCE', 'MIN_YEARS_EXPERIENCE']].copy()

for col in ['MAX_YEARS_EXPERIENCE', 'MIN_YEARS_EXPERIENCE', 'SALARY']:
    features[col] = pd.to_numeric(features[col], errors='coerce')

features = features.dropna()

scaler = StandardScaler()
X = scaler.fit_transform(features)

kmeans = KMeans(n_clusters=4, random_state=688)
eda.loc[features.index, 'Cluster'] = kmeans.fit_predict(X)

true_labels = eda.loc[features.index, 'SOC_2021_4_NAME']
true_labels_encoded = LabelEncoder().fit_transform(true_labels)

ari = adjusted_rand_score(true_labels_encoded, eda.loc[features.index, 'Cluster'])

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[2], line 1
----> 1 features = eda[['SALARY', 'MAX_YEARS_EXPERIENCE', 'MIN_YEARS_EXPERIENCE']].copy()
      3 for col in ['MAX_YEARS_EXPERIENCE', 'MIN_YEARS_EXPERIENCE', 'SALARY']:
      4     features[col] = pd.to_numeric(features[col], errors='coerce')

NameError: name 'eda' is not defined

import plotly.express as px
import plotly.graph_objects as go
from IPython.display import HTML

# 1) Build the DataFrame
df_plot = features.copy()
df_plot['Cluster'] = eda.loc[features.index, 'Cluster']

# 2) Compute centroids in original units
centroids = kmeans.cluster_centers_
centroids_x = centroids[:, 0] * X.std(axis=0)[0] + X.mean(axis=0)[0]
centroids_y = centroids[:, 1] * X.std(axis=0)[1] + X.mean(axis=0)[1]

# 3) Create an interactive Plotly Figure
fig = px.scatter(
    df_plot,
    x='SALARY',
    y='MAX_YEARS_EXPERIENCE',
    color='Cluster',
    title="KMeans Clustering by Salary and Max Years Experience",
    labels={
        'SALARY': 'Salary',
        'MAX_YEARS_EXPERIENCE': 'Max Years Experience',
        'Cluster': 'Cluster'
    },
    width=800,
    height=500,
)

# add centroids
fig.add_trace(
    go.Scatter(
        x=centroids_x,
        y=centroids_y,
        mode='markers',
        marker=dict(symbol='x', size=18, color='black',
                    line=dict(width=2, color='white')),
        name='Centroids'
    )
)

fig.update_layout(
    autosize=True,
    height=800,  
    margin=dict(l=20, r=20, t=50, b=20)
)

fig.write_html(
    "figures/analytics_plot1.html",
    include_plotlyjs=True,   
    full_html=False
)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[3], line 6
      3 from IPython.display import HTML
      5 # 1) Build the DataFrame
----> 6 df_plot = features.copy()
      7 df_plot['Cluster'] = eda.loc[features.index, 'Cluster']
      9 # 2) Compute centroids in original units

NameError: name 'features' is not defined

Here we have 4 cluster groups. Group 0, which represent as green have lower salary, mostly under 150k, and max years experience in 2-5 years, it is likely Likely junior to mid-level employees with moderate pay. Group 1 with orange, has medium to high salary, wide range from $100k–$500k and with narrow range ~3 years, they are suggests specialized or high-paying roles with short experience — possibly fast-track promotions or high-demand fields. cluster 2 are low salary and experience from 0-4 years, they are clearly entry level employee. cluster 3 has medium salary, mostly under 200k with higher experiences, like 6-13 eyars. They probably are senior professionals with more experience but not the highest salaries.

import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score
import plotly.graph_objects as go

# Prepare features & target
features = eda[['MIN_YEARS_EXPERIENCE', 'MAX_YEARS_EXPERIENCE']].apply(pd.to_numeric, errors='coerce')
features = features.dropna()
X = features
y = eda.loc[X.index, 'SALARY']

# Train/test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=688)

# Fit model & predict
model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

# Metrics (optional, but handy)
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print(f"MSE: {mse:.2f}, R²: {r2:.3f}")

# Define min/max for the identity line
min_val = y_test.min()
max_val = y_test.max()

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[4], line 8
      5 import plotly.graph_objects as go
      7 # Prepare features & target
----> 8 features = eda[['MIN_YEARS_EXPERIENCE', 'MAX_YEARS_EXPERIENCE']].apply(pd.to_numeric, errors='coerce')
      9 features = features.dropna()
     10 X = features

NameError: name 'eda' is not defined

fig = go.Figure([
    go.Scatter(
        x=y_test,
        y=y_pred,
        mode='markers',
        marker=dict(color='skyblue', opacity=0.6),
        name='Predicted vs Actual'
    ),
    go.Scatter(
        x=[min_val, max_val],
        y=[min_val, max_val],
        mode='lines',
        line=dict(color='red', dash='dash'),
        name='Ideal Fit'
    )
])

fig.update_layout(
    autosize=True,
    height=600,  # or whatever default height you prefer
    margin=dict(l=20, r=20, t=50, b=20)
)

fig.write_html(
    'figures/analytics_plot2.html',
    full_html=False,
    include_plotlyjs="cdn",
    config={"responsive": True}
)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[5], line 3
      1 fig = go.Figure([
      2     go.Scatter(
----> 3         x=y_test,
      4         y=y_pred,
      5         mode='markers',
      6         marker=dict(color='skyblue', opacity=0.6),
      7         name='Predicted vs Actual'
      8     ),
      9     go.Scatter(
     10         x=[min_val, max_val],
     11         y=[min_val, max_val],
     12         mode='lines',
     13         line=dict(color='red', dash='dash'),
     14         name='Ideal Fit'
     15     )
     16 ])
     18 fig.update_layout(
     19     autosize=True,
     20     height=600,  # or whatever default height you prefer
     21     margin=dict(l=20, r=20, t=50, b=20)
     22 )
     24 fig.write_html(
     25     'figures/analytics_plot2.html',
     26     full_html=False,
     27     include_plotlyjs="cdn",
     28     config={"responsive": True}
     29 )

NameError: name 'y_test' is not defined

This plot shows the Actual vs. Predicted Salary using a multiple linear regression model. The blue dots represent individual predictions, and the red dashed line is the ideal line where predicted = actual. Since most points lie very close to the red line, it means your model predicts salary very accurately, with minimal error and strong linear fit — likely reflected in a high R² score near 1.0.